Examining the Effect of Missing Data and Unmeasured Confounding on External Comparator Studies: Case Studies and Simulations

Missing data and unmeasured confounding are key challenges for external comparator studies. This work evaluates bias and other performance characteristics depending on missingness and unmeasured confounding by means of two case studies and simulations.